Overview

Dataset statistics

Number of variables21
Number of observations528
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory86.8 KiB
Average record size in memory168.2 B

Variable types

Categorical2
Text2
Numeric17

Alerts

ALL SEXES (RURAL) is highly overall correlated with FEMALE (RURAL) and 3 other fieldsHigh correlation
ALL SEXES (URBAN) is highly overall correlated with ANNUAL GROWTH RATE (URBAN) and 6 other fieldsHigh correlation
ANNUAL GROWTH RATE (URBAN) is highly overall correlated with ALL SEXES (URBAN) and 6 other fieldsHigh correlation
AVG HOUSEHOLD SIZE (URBAN) is highly overall correlated with ALL SEXES (URBAN) and 5 other fieldsHigh correlation
DIVISION is highly overall correlated with PROVINCE and 1 other fieldsHigh correlation
FEMALE (RURAL) is highly overall correlated with ALL SEXES (RURAL) and 3 other fieldsHigh correlation
FEMALE (URBAN) is highly overall correlated with ALL SEXES (URBAN) and 6 other fieldsHigh correlation
MALE (RURAL) is highly overall correlated with ALL SEXES (RURAL) and 3 other fieldsHigh correlation
MALE (URBAN) is highly overall correlated with ALL SEXES (URBAN) and 6 other fieldsHigh correlation
POPULATION 1998 (RURAL) is highly overall correlated with ALL SEXES (RURAL) and 3 other fieldsHigh correlation
POPULATION 1998 (URBAN) is highly overall correlated with ALL SEXES (URBAN) and 6 other fieldsHigh correlation
PROVINCE is highly overall correlated with DIVISIONHigh correlation
SEX RATIO (RURAL) is highly overall correlated with DIVISIONHigh correlation
SEX RATIO (URBAN) is highly overall correlated with ALL SEXES (URBAN) and 6 other fieldsHigh correlation
TRANSGENDER (RURAL) is highly overall correlated with ALL SEXES (RURAL) and 3 other fieldsHigh correlation
TRANSGENDER (URBAN) is highly overall correlated with ALL SEXES (URBAN) and 5 other fieldsHigh correlation
SUB DIVISION has unique valuesUnique
ALL SEXES (RURAL) has 34 (6.4%) zerosZeros
MALE (RURAL) has 34 (6.4%) zerosZeros
FEMALE (RURAL) has 34 (6.4%) zerosZeros
TRANSGENDER (RURAL) has 111 (21.0%) zerosZeros
SEX RATIO (RURAL) has 34 (6.4%) zerosZeros
AVG HOUSEHOLD SIZE (RURAL) has 34 (6.4%) zerosZeros
POPULATION 1998 (RURAL) has 22 (4.2%) zerosZeros
ANNUAL GROWTH RATE (RURAL) has 29 (5.5%) zerosZeros
ALL SEXES (URBAN) has 155 (29.4%) zerosZeros
MALE (URBAN) has 155 (29.4%) zerosZeros
FEMALE (URBAN) has 155 (29.4%) zerosZeros
TRANSGENDER (URBAN) has 203 (38.4%) zerosZeros
SEX RATIO (URBAN) has 155 (29.4%) zerosZeros
AVG HOUSEHOLD SIZE (URBAN) has 155 (29.4%) zerosZeros
POPULATION 1998 (URBAN) has 193 (36.6%) zerosZeros
ANNUAL GROWTH RATE (URBAN) has 193 (36.6%) zerosZeros

Reproduction

Analysis started2026-01-20 09:57:02.928052
Analysis finished2026-01-20 09:58:16.777585
Duration1 minute and 13.85 seconds
Software versionydata-profiling v4.18.0
Download configurationconfig.json

Variables

PROVINCE
Categorical

High correlation 

Distinct5
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
PUNJAB
143 
SINDH
137 
BALOCHISTAN
131 
KPK
71 
KPK/FATA
46 

Length

Max length11
Median length8
Mean length6.7518939
Min length3

Characters and Unicode

Total characters3565
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPUNJAB
2nd rowPUNJAB
3rd rowPUNJAB
4th rowPUNJAB
5th rowPUNJAB

Common Values

ValueCountFrequency (%)
PUNJAB143
27.1%
SINDH137
25.9%
BALOCHISTAN131
24.8%
KPK71
13.4%
KPK/FATA46
 
8.7%

Length

2026-01-20T14:58:17.004033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-20T14:58:17.172037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
punjab143
27.1%
sindh137
25.9%
balochistan131
24.8%
kpk71
13.4%
kpk/fata46
 
8.7%

Most occurring characters

ValueCountFrequency (%)
A497
13.9%
N411
11.5%
B274
 
7.7%
S268
 
7.5%
I268
 
7.5%
H268
 
7.5%
P260
 
7.3%
K234
 
6.6%
T177
 
5.0%
J143
 
4.0%
Other values (7)765
21.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)3565
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A497
13.9%
N411
11.5%
B274
 
7.7%
S268
 
7.5%
I268
 
7.5%
H268
 
7.5%
P260
 
7.3%
K234
 
6.6%
T177
 
5.0%
J143
 
4.0%
Other values (7)765
21.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3565
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A497
13.9%
N411
11.5%
B274
 
7.7%
S268
 
7.5%
I268
 
7.5%
H268
 
7.5%
P260
 
7.3%
K234
 
6.6%
T177
 
5.0%
J143
 
4.0%
Other values (7)765
21.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3565
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A497
13.9%
N411
11.5%
B274
 
7.7%
S268
 
7.5%
I268
 
7.5%
H268
 
7.5%
P260
 
7.3%
K234
 
6.6%
T177
 
5.0%
J143
 
4.0%
Other values (7)765
21.5%

DIVISION
Categorical

High correlation 

Distinct28
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
Quetta Division
38 
Makran Division
37 
MALAKAND DIVISION
 
33
Karachi Division
 
30
Hyderabad Division
 
30
Other values (23)
360 

Length

Max length28
Median length19
Mean length16.914773
Min length13

Characters and Unicode

Total characters8931
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBAHAWALPUR DIVISION
2nd rowBAHAWALPUR DIVISION
3rd rowBAHAWALPUR DIVISION
4th rowBAHAWALPUR DIVISION
5th rowBAHAWALPUR DIVISION

Common Values

ValueCountFrequency (%)
Quetta Division38
 
7.2%
Makran Division37
 
7.0%
MALAKAND DIVISION33
 
6.2%
Karachi Division30
 
5.7%
Hyderabad Division30
 
5.7%
RAWALPINDI DIVISION22
 
4.2%
Larkana Division21
 
4.0%
GUJRANWALA DIVISION20
 
3.8%
Zhob Division19
 
3.6%
Naseerabad Division19
 
3.6%
Other values (18)259
49.1%

Length

2026-01-20T14:58:17.404947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
division528
48.0%
quetta38
 
3.5%
makran37
 
3.4%
malakand33
 
3.0%
karachi30
 
2.7%
hyderabad30
 
2.7%
rawalpindi22
 
2.0%
larkana21
 
1.9%
gujranwala20
 
1.8%
zhob19
 
1.7%
Other values (22)323
29.3%

Most occurring characters

ValueCountFrequency (%)
I878
 
9.8%
i872
 
9.8%
D653
 
7.3%
A578
 
6.5%
573
 
6.4%
a471
 
5.3%
N434
 
4.9%
S367
 
4.1%
n346
 
3.9%
O308
 
3.4%
Other values (34)3451
38.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)8931
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I878
 
9.8%
i872
 
9.8%
D653
 
7.3%
A578
 
6.5%
573
 
6.4%
a471
 
5.3%
N434
 
4.9%
S367
 
4.1%
n346
 
3.9%
O308
 
3.4%
Other values (34)3451
38.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8931
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I878
 
9.8%
i872
 
9.8%
D653
 
7.3%
A578
 
6.5%
573
 
6.4%
a471
 
5.3%
N434
 
4.9%
S367
 
4.1%
n346
 
3.9%
O308
 
3.4%
Other values (34)3451
38.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8931
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I878
 
9.8%
i872
 
9.8%
D653
 
7.3%
A578
 
6.5%
573
 
6.4%
a471
 
5.3%
N434
 
4.9%
S367
 
4.1%
n346
 
3.9%
O308
 
3.4%
Other values (34)3451
38.6%
Distinct131
Distinct (%)24.8%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2026-01-20T14:58:18.258313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length28
Median length24
Mean length17.636364
Min length13

Characters and Unicode

Total characters9312
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)1.9%

Sample

1st rowBAHAWALNAGAR DISTRICT
2nd rowBAHAWALNAGAR DISTRICT
3rd rowBAHAWALNAGAR DISTRICT
4th rowBAHAWALNAGAR DISTRICT
5th rowBAHAWALNAGAR DISTRICT
ValueCountFrequency (%)
district528
43.4%
karachi21
 
1.7%
waziristan17
 
1.4%
dera17
 
1.4%
khan16
 
1.3%
south13
 
1.1%
kot11
 
0.9%
killa10
 
0.8%
dir10
 
0.8%
khuzdar9
 
0.7%
Other values (137)564
46.4%
2026-01-20T14:58:19.245633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
I1337
14.4%
T1251
13.4%
R930
10.0%
A902
9.7%
S737
7.9%
D689
7.4%
688
7.4%
C594
 
6.4%
H367
 
3.9%
K245
 
2.6%
Other values (17)1572
16.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)9312
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I1337
14.4%
T1251
13.4%
R930
10.0%
A902
9.7%
S737
7.9%
D689
7.4%
688
7.4%
C594
 
6.4%
H367
 
3.9%
K245
 
2.6%
Other values (17)1572
16.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)9312
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I1337
14.4%
T1251
13.4%
R930
10.0%
A902
9.7%
S737
7.9%
D689
7.4%
688
7.4%
C594
 
6.4%
H367
 
3.9%
K245
 
2.6%
Other values (17)1572
16.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)9312
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I1337
14.4%
T1251
13.4%
R930
10.0%
A902
9.7%
S737
7.9%
D689
7.4%
688
7.4%
C594
 
6.4%
H367
 
3.9%
K245
 
2.6%
Other values (17)1572
16.9%

SUB DIVISION
Text

Unique 

Distinct528
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2026-01-20T14:58:20.049772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length27
Median length23
Mean length15.660985
Min length8

Characters and Unicode

Total characters8269
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique528 ?
Unique (%)100.0%

Sample

1st rowBAHAWALNAGAR TEHSIL
2nd rowCHISHTIAN TEHSIL
3rd rowFORT ABBAS TEHSIL
4th rowHAROONABAD TEHSIL
5th rowMINCHINABAD TEHSIL
ValueCountFrequency (%)
tehsil358
29.1%
taluka106
 
8.6%
sub-tehsil56
 
4.5%
kot13
 
1.1%
khan13
 
1.1%
shah9
 
0.7%
city8
 
0.6%
tando5
 
0.4%
mirpur5
 
0.4%
zai5
 
0.4%
Other values (586)654
53.1%
2026-01-20T14:58:21.355503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A1093
13.2%
I721
8.7%
H720
8.7%
L713
8.6%
704
 
8.5%
T683
 
8.3%
S635
 
7.7%
E545
 
6.6%
R350
 
4.2%
U326
 
3.9%
Other values (26)1779
21.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)8269
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A1093
13.2%
I721
8.7%
H720
8.7%
L713
8.6%
704
 
8.5%
T683
 
8.3%
S635
 
7.7%
E545
 
6.6%
R350
 
4.2%
U326
 
3.9%
Other values (26)1779
21.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8269
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A1093
13.2%
I721
8.7%
H720
8.7%
L713
8.6%
704
 
8.5%
T683
 
8.3%
S635
 
7.7%
E545
 
6.6%
R350
 
4.2%
U326
 
3.9%
Other values (26)1779
21.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8269
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A1093
13.2%
I721
8.7%
H720
8.7%
L713
8.6%
704
 
8.5%
T683
 
8.3%
S635
 
7.7%
E545
 
6.6%
R350
 
4.2%
U326
 
3.9%
Other values (26)1779
21.5%

AREA (sq.km)
Real number (ℝ)

Distinct466
Distinct (%)88.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1492.0059
Minimum0
Maximum18374
Zeros4
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2026-01-20T14:58:21.600817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile41.7
Q1425
median882
Q31734.25
95-th percentile4403.55
Maximum18374
Range18374
Interquartile range (IQR)1309.25

Descriptive statistics

Standard deviation2039.4538
Coefficient of variation (CV)1.3669207
Kurtosis22.638661
Mean1492.0059
Median Absolute Deviation (MAD)548
Skewness4.1005685
Sum787779.1
Variance4159371.7
MonotonicityNot monotonic
2026-01-20T14:58:21.956172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4194
 
0.8%
04
 
0.8%
2503
 
0.6%
7623
 
0.6%
4183
 
0.6%
2972
 
0.4%
4542
 
0.4%
3512
 
0.4%
7592
 
0.4%
2162
 
0.4%
Other values (456)501
94.9%
ValueCountFrequency (%)
04
0.8%
42
0.4%
62
0.4%
81
 
0.2%
92
0.4%
111
 
0.2%
131
 
0.2%
141
 
0.2%
181
 
0.2%
192
0.4%
ValueCountFrequency (%)
183741
0.2%
160921
0.2%
153631
0.2%
130751
0.2%
116631
0.2%
116111
0.2%
97421
0.2%
94391
0.2%
93221
0.2%
93181
0.2%

ALL SEXES (RURAL)
Real number (ℝ)

High correlation  Zeros 

Distinct495
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean246278.01
Minimum0
Maximum2297375
Zeros34
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2026-01-20T14:58:22.244140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q150934.5
median165241
Q3312911.25
95-th percentile790803.6
Maximum2297375
Range2297375
Interquartile range (IQR)261976.75

Descriptive statistics

Standard deviation271189.82
Coefficient of variation (CV)1.1011532
Kurtosis8.2134366
Mean246278.01
Median Absolute Deviation (MAD)124502
Skewness2.2640217
Sum1.3003479 × 108
Variance7.3543917 × 1010
MonotonicityNot monotonic
2026-01-20T14:58:22.542450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
034
 
6.4%
6195501
 
0.2%
3113551
 
0.2%
194971
 
0.2%
5277161
 
0.2%
7233611
 
0.2%
190111
 
0.2%
184521
 
0.2%
2720631
 
0.2%
974691
 
0.2%
Other values (485)485
91.9%
ValueCountFrequency (%)
034
6.4%
26651
 
0.2%
28681
 
0.2%
51841
 
0.2%
57211
 
0.2%
75831
 
0.2%
78811
 
0.2%
84231
 
0.2%
85841
 
0.2%
94411
 
0.2%
ValueCountFrequency (%)
22973751
0.2%
14251541
0.2%
13851091
0.2%
12673171
0.2%
12387081
0.2%
11588171
0.2%
11152871
0.2%
10933541
0.2%
10678801
0.2%
10455771
0.2%

MALE (RURAL)
Real number (ℝ)

High correlation  Zeros 

Distinct495
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean125275.69
Minimum0
Maximum1172995
Zeros34
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2026-01-20T14:58:22.893242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q127127.25
median84134.5
Q3160502.25
95-th percentile402758.6
Maximum1172995
Range1172995
Interquartile range (IQR)133375

Descriptive statistics

Standard deviation137563.02
Coefficient of variation (CV)1.0980824
Kurtosis8.4907615
Mean125275.69
Median Absolute Deviation (MAD)63141
Skewness2.2942179
Sum66145563
Variance1.8923585 × 1010
MonotonicityNot monotonic
2026-01-20T14:58:23.202065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
034
 
6.4%
3168641
 
0.2%
1646561
 
0.2%
105831
 
0.2%
2770241
 
0.2%
3757101
 
0.2%
97231
 
0.2%
96151
 
0.2%
1407881
 
0.2%
511491
 
0.2%
Other values (485)485
91.9%
ValueCountFrequency (%)
034
6.4%
14411
 
0.2%
14471
 
0.2%
26451
 
0.2%
31121
 
0.2%
40321
 
0.2%
41501
 
0.2%
43341
 
0.2%
43401
 
0.2%
48401
 
0.2%
ValueCountFrequency (%)
11729951
0.2%
7307291
0.2%
7118661
0.2%
6510511
0.2%
6361501
0.2%
5901021
0.2%
5604091
0.2%
5471121
0.2%
5445901
0.2%
5368871
0.2%

FEMALE (RURAL)
Real number (ℝ)

High correlation  Zeros 

Distinct495
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120984.15
Minimum0
Maximum1124167
Zeros34
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2026-01-20T14:58:23.478974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q123979
median82044
Q3152219
95-th percentile393597.55
Maximum1124167
Range1124167
Interquartile range (IQR)128240

Descriptive statistics

Standard deviation133716.9
Coefficient of variation (CV)1.1052431
Kurtosis7.9264432
Mean120984.15
Median Absolute Deviation (MAD)62102.5
Skewness2.2341756
Sum63879631
Variance1.7880209 × 1010
MonotonicityNot monotonic
2026-01-20T14:58:23.849163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
034
 
6.4%
3026441
 
0.2%
1466991
 
0.2%
89141
 
0.2%
2506081
 
0.2%
3475991
 
0.2%
92871
 
0.2%
88361
 
0.2%
1312601
 
0.2%
463151
 
0.2%
Other values (485)485
91.9%
ValueCountFrequency (%)
034
6.4%
12231
 
0.2%
14201
 
0.2%
25391
 
0.2%
26091
 
0.2%
35511
 
0.2%
37311
 
0.2%
40831
 
0.2%
42501
 
0.2%
44951
 
0.2%
ValueCountFrequency (%)
11241671
0.2%
6942801
0.2%
6731331
0.2%
6161471
0.2%
6023911
0.2%
5705461
0.2%
5685681
0.2%
5328941
0.2%
5206861
0.2%
5185771
0.2%

TRANSGENDER (RURAL)
Real number (ℝ)

High correlation  Zeros 

Distinct82
Distinct (%)15.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.174242
Minimum0
Maximum213
Zeros111
Zeros (%)21.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2026-01-20T14:58:24.144810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median10
Q325
95-th percentile66.3
Maximum213
Range213
Interquartile range (IQR)24

Descriptive statistics

Standard deviation25.522248
Coefficient of variation (CV)1.4043088
Kurtosis12.816648
Mean18.174242
Median Absolute Deviation (MAD)10
Skewness2.9579887
Sum9596
Variance651.38514
MonotonicityNot monotonic
2026-01-20T14:58:24.395095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0111
21.0%
137
 
7.0%
219
 
3.6%
318
 
3.4%
616
 
3.0%
515
 
2.8%
415
 
2.8%
1113
 
2.5%
1412
 
2.3%
812
 
2.3%
Other values (72)260
49.2%
ValueCountFrequency (%)
0111
21.0%
137
 
7.0%
219
 
3.6%
318
 
3.4%
415
 
2.8%
515
 
2.8%
616
 
3.0%
712
 
2.3%
812
 
2.3%
98
 
1.5%
ValueCountFrequency (%)
2131
0.2%
1671
0.2%
1511
0.2%
1471
0.2%
1451
0.2%
1192
0.4%
1102
0.4%
1041
0.2%
1021
0.2%
862
0.4%

SEX RATIO (RURAL)
Real number (ℝ)

High correlation  Zeros 

Distinct435
Distinct (%)82.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.982614
Minimum0
Maximum139.38
Zeros34
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2026-01-20T14:58:24.717074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1101.105
median105.285
Q3108.3475
95-th percentile117.9695
Maximum139.38
Range139.38
Interquartile range (IQR)7.2425

Descriptive statistics

Standard deviation26.81266
Coefficient of variation (CV)0.27088252
Kurtosis9.1632853
Mean98.982614
Median Absolute Deviation (MAD)3.59
Skewness-3.1851718
Sum52262.82
Variance718.91873
MonotonicityNot monotonic
2026-01-20T14:58:25.055580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
034
 
6.4%
104.173
 
0.6%
110.973
 
0.6%
104.533
 
0.6%
106.273
 
0.6%
105.033
 
0.6%
109.112
 
0.4%
108.92
 
0.4%
105.632
 
0.4%
106.052
 
0.4%
Other values (425)471
89.2%
ValueCountFrequency (%)
034
6.4%
87.651
 
0.2%
89.151
 
0.2%
90.561
 
0.2%
90.761
 
0.2%
90.871
 
0.2%
91.392
 
0.4%
91.451
 
0.2%
91.721
 
0.2%
91.851
 
0.2%
ValueCountFrequency (%)
139.381
0.2%
134.771
0.2%
131.621
0.2%
129.291
0.2%
127.521
0.2%
125.911
0.2%
124.51
0.2%
123.71
0.2%
123.091
0.2%
123.081
0.2%

AVG HOUSEHOLD SIZE (RURAL)
Real number (ℝ)

Zeros 

Distinct277
Distinct (%)52.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2770644
Minimum0
Maximum12.43
Zeros34
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2026-01-20T14:58:25.302722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.7
median6.31
Q37.2
95-th percentile9.2125
Maximum12.43
Range12.43
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation2.0749469
Coefficient of variation (CV)0.33056007
Kurtosis3.3297221
Mean6.2770644
Median Absolute Deviation (MAD)0.765
Skewness-1.3125969
Sum3314.29
Variance4.3054045
MonotonicityNot monotonic
2026-01-20T14:58:25.663880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
034
 
6.4%
6.696
 
1.1%
6.626
 
1.1%
5.865
 
0.9%
5.245
 
0.9%
6.315
 
0.9%
5.895
 
0.9%
6.375
 
0.9%
6.024
 
0.8%
6.124
 
0.8%
Other values (267)449
85.0%
ValueCountFrequency (%)
034
6.4%
4.491
 
0.2%
4.531
 
0.2%
4.581
 
0.2%
4.691
 
0.2%
4.741
 
0.2%
4.822
 
0.4%
4.891
 
0.2%
4.912
 
0.4%
4.961
 
0.2%
ValueCountFrequency (%)
12.431
0.2%
11.061
0.2%
11.041
0.2%
10.981
0.2%
10.671
0.2%
10.241
0.2%
10.191
0.2%
10.171
0.2%
10.121
0.2%
10.031
0.2%

POPULATION 1998 (RURAL)
Real number (ℝ)

High correlation  Zeros 

Distinct507
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167427.99
Minimum0
Maximum1044035
Zeros22
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2026-01-20T14:58:26.193732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3938.7
Q135273.5
median117206.5
Q3213054.25
95-th percentile571231.25
Maximum1044035
Range1044035
Interquartile range (IQR)177780.75

Descriptive statistics

Standard deviation178388.98
Coefficient of variation (CV)1.0654668
Kurtosis3.636449
Mean167427.99
Median Absolute Deviation (MAD)86133.5
Skewness1.8361336
Sum88401981
Variance3.1822627 × 1010
MonotonicityNot monotonic
2026-01-20T14:58:26.491900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
022
 
4.2%
4077681
 
0.2%
225111
 
0.2%
288671
 
0.2%
1083061
 
0.2%
633751
 
0.2%
580931
 
0.2%
951321
 
0.2%
740821
 
0.2%
1109371
 
0.2%
Other values (497)497
94.1%
ValueCountFrequency (%)
022
4.2%
991
 
0.2%
28331
 
0.2%
30241
 
0.2%
32471
 
0.2%
39171
 
0.2%
39791
 
0.2%
40571
 
0.2%
51651
 
0.2%
53051
 
0.2%
ValueCountFrequency (%)
10440351
0.2%
9248491
0.2%
9009841
0.2%
8425631
0.2%
8268631
0.2%
8210891
0.2%
8031591
0.2%
7678681
0.2%
7298851
0.2%
7143411
0.2%

ANNUAL GROWTH RATE (RURAL)
Real number (ℝ)

Zeros 

Distinct275
Distinct (%)52.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1247917
Minimum0
Maximum100
Zeros29
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2026-01-20T14:58:26.842188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.44
median2.03
Q32.8125
95-th percentile4.733
Maximum100
Range100
Interquartile range (IQR)1.3725

Descriptive statistics

Standard deviation9.5778717
Coefficient of variation (CV)3.0651233
Kurtosis97.420256
Mean3.1247917
Median Absolute Deviation (MAD)0.67
Skewness9.8529218
Sum1649.89
Variance91.735627
MonotonicityNot monotonic
2026-01-20T14:58:27.142643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
029
 
5.5%
1.447
 
1.3%
2.76
 
1.1%
1005
 
0.9%
1.784
 
0.8%
1.884
 
0.8%
1.24
 
0.8%
1.534
 
0.8%
1.984
 
0.8%
3.154
 
0.8%
Other values (265)457
86.6%
ValueCountFrequency (%)
029
5.5%
0.022
 
0.4%
0.111
 
0.2%
0.152
 
0.4%
0.191
 
0.2%
0.221
 
0.2%
0.31
 
0.2%
0.321
 
0.2%
0.361
 
0.2%
0.381
 
0.2%
ValueCountFrequency (%)
1005
0.9%
12.741
 
0.2%
8.071
 
0.2%
7.831
 
0.2%
7.231
 
0.2%
7.081
 
0.2%
7.071
 
0.2%
6.931
 
0.2%
6.841
 
0.2%
6.431
 
0.2%

ALL SEXES (URBAN)
Real number (ℝ)

High correlation  Zeros 

Distinct373
Distinct (%)70.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean140863.53
Minimum0
Maximum3653616
Zeros155
Zeros (%)29.4%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2026-01-20T14:58:27.408931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median43254.5
Q3117814.75
95-th percentile588102.2
Maximum3653616
Range3653616
Interquartile range (IQR)117814.75

Descriptive statistics

Standard deviation351246.28
Coefficient of variation (CV)2.4935218
Kurtosis43.011543
Mean140863.53
Median Absolute Deviation (MAD)43254.5
Skewness5.9088136
Sum74375943
Variance1.2337395 × 1011
MonotonicityNot monotonic
2026-01-20T14:58:27.780464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0155
29.4%
676862
 
0.4%
1938401
 
0.2%
272081
 
0.2%
658251
 
0.2%
2793381
 
0.2%
513061
 
0.2%
933411
 
0.2%
1520251
 
0.2%
569221
 
0.2%
Other values (363)363
68.8%
ValueCountFrequency (%)
0155
29.4%
33921
 
0.2%
42701
 
0.2%
43641
 
0.2%
55021
 
0.2%
63811
 
0.2%
74841
 
0.2%
76791
 
0.2%
78151
 
0.2%
92491
 
0.2%
ValueCountFrequency (%)
36536161
0.2%
32101581
0.2%
27035691
0.2%
22815571
0.2%
21653401
0.2%
20978241
0.2%
19698231
0.2%
18270011
0.2%
16327021
0.2%
11261411
0.2%

MALE (URBAN)
Real number (ℝ)

High correlation  Zeros 

Distinct372
Distinct (%)70.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72843.386
Minimum0
Maximum1905921
Zeros155
Zeros (%)29.4%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2026-01-20T14:58:28.084290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median21980
Q360301.75
95-th percentile303676.9
Maximum1905921
Range1905921
Interquartile range (IQR)60301.75

Descriptive statistics

Standard deviation182349.24
Coefficient of variation (CV)2.5033054
Kurtosis43.101806
Mean72843.386
Median Absolute Deviation (MAD)21980
Skewness5.9119247
Sum38461308
Variance3.3251246 × 1010
MonotonicityNot monotonic
2026-01-20T14:58:28.370825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0155
29.4%
301352
 
0.4%
348852
 
0.4%
983911
 
0.2%
480291
 
0.2%
448461
 
0.2%
340111
 
0.2%
1439111
 
0.2%
264471
 
0.2%
481521
 
0.2%
Other values (362)362
68.6%
ValueCountFrequency (%)
0155
29.4%
16861
 
0.2%
22011
 
0.2%
27091
 
0.2%
32671
 
0.2%
32921
 
0.2%
38891
 
0.2%
39581
 
0.2%
42621
 
0.2%
47821
 
0.2%
ValueCountFrequency (%)
19059211
0.2%
16516841
0.2%
14212911
0.2%
11812391
0.2%
11048981
0.2%
10819151
0.2%
10250101
0.2%
9370271
0.2%
8571331
0.2%
5947091
0.2%

FEMALE (URBAN)
Real number (ℝ)

High correlation  Zeros 

Distinct373
Distinct (%)70.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67997.866
Minimum0
Maximum1746900
Zeros155
Zeros (%)29.4%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2026-01-20T14:58:28.699583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median20999
Q357465.25
95-th percentile288513.15
Maximum1746900
Range1746900
Interquartile range (IQR)57465.25

Descriptive statistics

Standard deviation168872.53
Coefficient of variation (CV)2.4834975
Kurtosis42.921144
Mean67997.866
Median Absolute Deviation (MAD)20999
Skewness5.9054954
Sum35902873
Variance2.8517931 × 1010
MonotonicityNot monotonic
2026-01-20T14:58:29.050700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0155
29.4%
327992
 
0.4%
954021
 
0.2%
131031
 
0.2%
318081
 
0.2%
1353851
 
0.2%
248481
 
0.2%
451761
 
0.2%
737621
 
0.2%
276121
 
0.2%
Other values (363)363
68.8%
ValueCountFrequency (%)
0155
29.4%
10971
 
0.2%
17061
 
0.2%
20681
 
0.2%
27931
 
0.2%
30871
 
0.2%
32221
 
0.2%
37901
 
0.2%
38561
 
0.2%
43661
 
0.2%
ValueCountFrequency (%)
17469001
0.2%
15581061
0.2%
12817621
0.2%
10999561
0.2%
10601641
0.2%
10152011
0.2%
9444011
0.2%
8897631
0.2%
7753331
0.2%
5312531
0.2%

TRANSGENDER (URBAN)
Real number (ℝ)

High correlation  Zeros 

Distinct93
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.276515
Minimum0
Maximum795
Zeros203
Zeros (%)38.4%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2026-01-20T14:58:29.303085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q319
95-th percentile92.3
Maximum795
Range795
Interquartile range (IQR)19

Descriptive statistics

Standard deviation66.068127
Coefficient of variation (CV)2.9658197
Kurtosis67.425758
Mean22.276515
Median Absolute Deviation (MAD)3
Skewness7.3071948
Sum11762
Variance4364.9974
MonotonicityNot monotonic
2026-01-20T14:58:29.652177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0203
38.4%
227
 
5.1%
325
 
4.7%
418
 
3.4%
516
 
3.0%
913
 
2.5%
113
 
2.5%
611
 
2.1%
1011
 
2.1%
79
 
1.7%
Other values (83)182
34.5%
ValueCountFrequency (%)
0203
38.4%
113
 
2.5%
227
 
5.1%
325
 
4.7%
418
 
3.4%
516
 
3.0%
611
 
2.1%
79
 
1.7%
87
 
1.3%
913
 
2.5%
ValueCountFrequency (%)
7951
0.2%
7081
0.2%
5161
0.2%
4121
0.2%
3681
0.2%
3621
0.2%
2781
0.2%
2361
0.2%
2111
0.2%
1791
0.2%

SEX RATIO (URBAN)
Real number (ℝ)

High correlation  Zeros 

Distinct340
Distinct (%)64.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.411269
Minimum0
Maximum297.81
Zeros155
Zeros (%)29.4%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2026-01-20T14:58:30.012648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median103.195
Q3107.04
95-th percentile114.1445
Maximum297.81
Range297.81
Interquartile range (IQR)107.04

Descriptive statistics

Standard deviation49.687341
Coefficient of variation (CV)0.65888483
Kurtosis-0.51936296
Mean75.411269
Median Absolute Deviation (MAD)5.375
Skewness-0.66426102
Sum39817.15
Variance2468.8319
MonotonicityNot monotonic
2026-01-20T14:58:30.257739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0155
29.4%
103.133
 
0.6%
107.042
 
0.4%
105.642
 
0.4%
106.112
 
0.4%
102.652
 
0.4%
105.372
 
0.4%
105.832
 
0.4%
102.272
 
0.4%
100.962
 
0.4%
Other values (330)354
67.0%
ValueCountFrequency (%)
0155
29.4%
91.421
 
0.2%
91.771
 
0.2%
91.861
 
0.2%
91.951
 
0.2%
93.491
 
0.2%
94.211
 
0.2%
94.351
 
0.2%
94.711
 
0.2%
95.031
 
0.2%
ValueCountFrequency (%)
297.811
0.2%
157.831
0.2%
135.021
0.2%
132.731
0.2%
132.281
0.2%
131.571
0.2%
129.011
0.2%
128.071
0.2%
126.921
0.2%
124.851
0.2%

AVG HOUSEHOLD SIZE (URBAN)
Real number (ℝ)

High correlation  Zeros 

Distinct222
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4064015
Minimum0
Maximum10.06
Zeros155
Zeros (%)29.4%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2026-01-20T14:58:30.556830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5.755
Q36.34
95-th percentile7.8665
Maximum10.06
Range10.06
Interquartile range (IQR)6.34

Descriptive statistics

Standard deviation2.9483358
Coefficient of variation (CV)0.66910285
Kurtosis-1.1711406
Mean4.4064015
Median Absolute Deviation (MAD)0.845
Skewness-0.66045827
Sum2326.58
Variance8.6926842
MonotonicityNot monotonic
2026-01-20T14:58:31.109863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0155
29.4%
6.187
 
1.3%
6.025
 
0.9%
5.815
 
0.9%
6.345
 
0.9%
5.915
 
0.9%
5.925
 
0.9%
6.15
 
0.9%
6.325
 
0.9%
5.454
 
0.8%
Other values (212)327
61.9%
ValueCountFrequency (%)
0155
29.4%
4.561
 
0.2%
4.581
 
0.2%
4.71
 
0.2%
4.731
 
0.2%
4.752
 
0.4%
4.831
 
0.2%
4.872
 
0.4%
4.882
 
0.4%
4.91
 
0.2%
ValueCountFrequency (%)
10.061
0.2%
9.41
0.2%
9.361
0.2%
9.311
0.2%
9.221
0.2%
9.11
0.2%
9.021
0.2%
8.71
0.2%
8.532
0.4%
8.41
0.2%

POPULATION 1998 (URBAN)
Real number (ℝ)

High correlation  Zeros 

Distinct335
Distinct (%)63.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80144.566
Minimum0
Maximum2075867
Zeros193
Zeros (%)36.6%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2026-01-20T14:58:31.359997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median21298
Q365422.25
95-th percentile378772.55
Maximum2075867
Range2075867
Interquartile range (IQR)65422.25

Descriptive statistics

Standard deviation202312.02
Coefficient of variation (CV)2.5243386
Kurtosis42.901156
Mean80144.566
Median Absolute Deviation (MAD)21298
Skewness5.7902234
Sum42316331
Variance4.0930153 × 1010
MonotonicityNot monotonic
2026-01-20T14:58:31.722709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0193
36.6%
431792
 
0.4%
1337851
 
0.2%
99781
 
0.2%
403551
 
0.2%
610331
 
0.2%
306861
 
0.2%
219571
 
0.2%
383731
 
0.2%
662471
 
0.2%
Other values (325)325
61.6%
ValueCountFrequency (%)
0193
36.6%
6361
 
0.2%
25131
 
0.2%
42491
 
0.2%
51091
 
0.2%
57881
 
0.2%
59651
 
0.2%
73731
 
0.2%
76701
 
0.2%
76791
 
0.2%
ValueCountFrequency (%)
20758671
0.2%
20088611
0.2%
14097681
0.2%
12337181
0.2%
12256021
0.2%
11361921
0.2%
10782451
0.2%
9828161
0.2%
7359971
0.2%
6374071
0.2%

ANNUAL GROWTH RATE (URBAN)
Real number (ℝ)

High correlation  Zeros 

Distinct233
Distinct (%)44.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9208144
Minimum0
Maximum19.78
Zeros193
Zeros (%)36.6%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2026-01-20T14:58:32.033024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.855
Q32.985
95-th percentile5.5125
Maximum19.78
Range19.78
Interquartile range (IQR)2.985

Descriptive statistics

Standard deviation2.0989081
Coefficient of variation (CV)1.0927178
Kurtosis10.763879
Mean1.9208144
Median Absolute Deviation (MAD)1.855
Skewness2.0938266
Sum1014.19
Variance4.4054151
MonotonicityNot monotonic
2026-01-20T14:58:32.300649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0193
36.6%
1.976
 
1.1%
2.015
 
0.9%
3.155
 
0.9%
2.534
 
0.8%
1.994
 
0.8%
2.394
 
0.8%
4.044
 
0.8%
2.624
 
0.8%
2.313
 
0.6%
Other values (223)296
56.1%
ValueCountFrequency (%)
0193
36.6%
0.191
 
0.2%
0.241
 
0.2%
0.271
 
0.2%
0.371
 
0.2%
0.462
 
0.4%
0.51
 
0.2%
0.521
 
0.2%
0.561
 
0.2%
0.591
 
0.2%
ValueCountFrequency (%)
19.781
0.2%
11.051
0.2%
9.961
0.2%
9.441
0.2%
9.191
0.2%
9.181
0.2%
8.541
0.2%
8.481
0.2%
8.071
0.2%
7.951
0.2%

Interactions

2026-01-20T14:58:11.168412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:04.519385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:08.875339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:12.968482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:16.862580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:21.022148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:24.702673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:29.013870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:32.849472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:36.563899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:40.748399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:44.910691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:49.133014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:53.753967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:58.430096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:58:02.846813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:58:06.967122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:58:11.397875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:04.834039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:09.119162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:13.240231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:17.078089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:21.237169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:24.985417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:29.246376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:33.063390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:37.028963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:41.008710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:45.146594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:49.362981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:54.009910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:58.720356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:58:03.064999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:58:07.179679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:58:11.689706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:05.095115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:09.321296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:13.441989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:17.259440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:21.425182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:25.165530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-20T14:57:33.273275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-20T14:57:45.347980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:49.654457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:54.217064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:58.969716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:58:03.268106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-20T14:58:11.997681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:05.344615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-20T14:57:13.688365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-20T14:57:54.653839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:59.175637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:58:03.517300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:58:07.638281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:58:12.272740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:05.556690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:09.745218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:13.926308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:17.674776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:21.890901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-20T14:57:14.123046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:17.943657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-20T14:57:25.876685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:30.137568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:33.938563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:38.001477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-20T14:57:46.081465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-20T14:58:04.008079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:58:08.100334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-20T14:57:06.132828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-20T14:57:10.922247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:15.016022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:18.937785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:23.012315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:26.982121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:31.025021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:34.778853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:38.874953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:42.801805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:47.039787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:51.442899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:56.294142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:58:00.609699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:58:05.039485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:58:09.040195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:58:14.002440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:07.110051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:11.149778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:15.232417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:19.146864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:23.207808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:27.207983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:31.244465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:35.031238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:39.139934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:43.059897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:47.252848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:51.740059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:56.600215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:58:00.896784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:58:05.254078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:58:09.252816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:58:14.233346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:07.350083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:11.373931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:15.449039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:19.355988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:23.432255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:27.431450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:31.460184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:35.243659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:39.408079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:43.290481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:47.560759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:52.023520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:56.901427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:58:01.107292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:58:05.517461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:58:09.512730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:58:14.506821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:07.584799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:11.578513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:15.701546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:19.563355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:23.642798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:27.655693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:31.706584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:35.461205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:39.637582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:43.510056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:47.860909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:52.222362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:57.128836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:58:01.596915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:58:05.796520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:58:09.811015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:58:14.809108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:07.918435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:11.867573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:15.965976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:19.848040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:23.907424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:27.926448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:31.973164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:35.711038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:39.895700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:43.800642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:48.110282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:52.509269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:57.367908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:58:01.902243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:58:06.042289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:58:10.061867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:58:15.083024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:08.151964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:12.087041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:16.166580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:20.078879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:24.104544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:28.157383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:32.175002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:35.941342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:40.107851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:44.023308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:48.332205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:53.000482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:57.648687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:58:02.099671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:58:06.239135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:58:10.449533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:58:15.291539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:08.349538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:12.493865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:16.366966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:20.311851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:24.278684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:28.365889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:32.360554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:36.148667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:40.309851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:44.245803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:48.618704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:53.196432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:57.930009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:58:02.297045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:58:06.451140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:58:10.718349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:58:15.553283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:08.578449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:12.704534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:16.562855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:20.748182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:24.483572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:28.546691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:32.566984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:36.348276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:40.497890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:44.456083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:48.888587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:53.444844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:57:58.128940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:58:02.552740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:58:06.689831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-20T14:58:10.952715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-01-20T14:58:32.614975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ALL SEXES (RURAL)ALL SEXES (URBAN)ANNUAL GROWTH RATE (RURAL)ANNUAL GROWTH RATE (URBAN)AREA (sq.km)AVG HOUSEHOLD SIZE (RURAL)AVG HOUSEHOLD SIZE (URBAN)DIVISIONFEMALE (RURAL)FEMALE (URBAN)MALE (RURAL)MALE (URBAN)POPULATION 1998 (RURAL)POPULATION 1998 (URBAN)PROVINCESEX RATIO (RURAL)SEX RATIO (URBAN)TRANSGENDER (RURAL)TRANSGENDER (URBAN)
ALL SEXES (RURAL)1.0000.3790.0800.3850.2420.2080.4210.3101.0000.3811.0000.3760.9460.3880.337-0.1930.1180.8470.379
ALL SEXES (URBAN)0.3791.000-0.2470.697-0.103-0.3550.6450.1940.3771.0000.3791.0000.4550.9380.119-0.3870.6620.3830.920
ANNUAL GROWTH RATE (RURAL)0.080-0.2471.000-0.0970.0940.341-0.0320.3240.074-0.2500.085-0.244-0.016-0.2350.0900.321-0.087-0.008-0.282
ANNUAL GROWTH RATE (URBAN)0.3850.697-0.0971.0000.028-0.2990.5990.2240.3830.6970.3860.6970.4370.6980.243-0.2080.5780.3770.620
AREA (sq.km)0.242-0.1030.0940.0281.0000.0410.0910.1220.235-0.1050.248-0.1010.218-0.1230.1590.3510.0130.217-0.087
AVG HOUSEHOLD SIZE (RURAL)0.208-0.3550.341-0.2990.0411.0000.0680.4740.211-0.3550.204-0.3560.113-0.3330.4800.035-0.4100.045-0.402
AVG HOUSEHOLD SIZE (URBAN)0.4210.645-0.0320.5990.0910.0681.0000.4720.4210.6450.4200.6450.4500.5780.499-0.2400.6130.3380.493
DIVISION0.3100.1940.3240.2240.1220.4740.4721.0000.3140.1980.3080.1940.3360.2200.8830.5270.3100.2600.112
FEMALE (RURAL)1.0000.3770.0740.3830.2350.2110.4210.3141.0000.3790.9980.3750.9480.3880.343-0.2110.1100.8470.378
FEMALE (URBAN)0.3811.000-0.2500.697-0.105-0.3550.6450.1980.3791.0000.3810.9990.4580.9380.119-0.3920.6540.3850.921
MALE (RURAL)1.0000.3790.0850.3860.2480.2040.4200.3080.9980.3811.0000.3770.9450.3880.338-0.1750.1250.8480.379
MALE (URBAN)0.3761.000-0.2440.697-0.101-0.3560.6450.1940.3750.9990.3771.0000.4520.9380.121-0.3820.6690.3800.918
POPULATION 1998 (RURAL)0.9460.455-0.0160.4370.2180.1130.4500.3360.9480.4580.9450.4521.0000.4470.413-0.2890.1550.8200.462
POPULATION 1998 (URBAN)0.3880.938-0.2350.698-0.123-0.3330.5780.2200.3880.9380.3880.9380.4471.0000.140-0.3980.5800.3880.885
PROVINCE0.3370.1190.0900.2430.1590.4800.4990.8830.3430.1190.3380.1210.4130.1401.0000.3030.3380.2820.070
SEX RATIO (RURAL)-0.193-0.3870.321-0.2080.3510.035-0.2400.527-0.211-0.392-0.175-0.382-0.289-0.3980.3031.000-0.023-0.148-0.377
SEX RATIO (URBAN)0.1180.662-0.0870.5780.013-0.4100.6130.3100.1100.6540.1250.6690.1550.5800.338-0.0231.0000.1410.549
TRANSGENDER (RURAL)0.8470.383-0.0080.3770.2170.0450.3380.2600.8470.3850.8480.3800.8200.3880.282-0.1480.1411.0000.453
TRANSGENDER (URBAN)0.3790.920-0.2820.620-0.087-0.4020.4930.1120.3780.9210.3790.9180.4620.8850.070-0.3770.5490.4531.000

Missing values

2026-01-20T14:58:16.033598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-20T14:58:16.451038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PROVINCEDIVISIONDISTRICTSUB DIVISIONAREA (sq.km)ALL SEXES (RURAL)MALE (RURAL)FEMALE (RURAL)TRANSGENDER (RURAL)SEX RATIO (RURAL)AVG HOUSEHOLD SIZE (RURAL)POPULATION 1998 (RURAL)ANNUAL GROWTH RATE (RURAL)ALL SEXES (URBAN)MALE (URBAN)FEMALE (URBAN)TRANSGENDER (URBAN)SEX RATIO (URBAN)AVG HOUSEHOLD SIZE (URBAN)POPULATION 1998 (URBAN)ANNUAL GROWTH RATE (URBAN)
0PUNJABBAHAWALPUR DIVISIONBAHAWALNAGAR DISTRICTBAHAWALNAGAR TEHSIL1729.061955031686430264442104.706.104077682.22193840983919540247103.136.021337851.97
1PUNJABBAHAWALPUR DIVISIONBAHAWALNAGAR DISTRICTCHISHTIAN TEHSIL1500.054034227378826650054102.736.153959831.65149424755467385127102.306.011022872.01
2PUNJABBAHAWALPUR DIVISIONBAHAWALNAGAR DISTRICTFORT ABBAS TEHSIL2536.036124018265517854144102.306.322509591.9361528313603015018104.016.00346373.06
3PUNJABBAHAWALPUR DIVISIONBAHAWALNAGAR DISTRICTHAROONABAD TEHSIL1295.038211519227818980829101.306.152973431.33142600713457123619100.156.02844242.79
4PUNJABBAHAWALPUR DIVISIONBAHAWALNAGAR DISTRICTMINCHINABAD TEHSIL1818.045272323150622117839104.676.293165931.9072294366943559010103.106.34376683.48
5PUNJABBAHAWALPUR DIVISIONBAHAWALPUR DISTRICTAHMADPUR EAST TEHSIL1738.090247646152544088962104.686.536014062.16176110900038608126104.566.281168912.18
6PUNJABBAHAWALPUR DIVISIONBAHAWALPUR DISTRICTBAHAWALPUR CITY TEHSIL1490.000000.000.00629160.0068211635018633184288105.535.913566263.47
7PUNJABBAHAWALPUR DIVISIONBAHAWALPUR DISTRICTBAHAWALPUR SADDAR TEHSIL745.046793823954122834651104.906.333144282.11107948620094592712135.025.98726102.11
8PUNJABBAHAWALPUR DIVISIONBAHAWALPUR DISTRICTHASILPUR TEHSIL1490.034074717111716962010100.886.032462181.7211561357743578531799.816.09712952.57
9PUNJABBAHAWALPUR DIVISIONBAHAWALPUR DISTRICTKHAIRPUR TAMEWALI TEHSIL993.02214311129521084763104.136.221570491.824149220981205074102.315.75268542.31
PROVINCEDIVISIONDISTRICTSUB DIVISIONAREA (sq.km)ALL SEXES (RURAL)MALE (RURAL)FEMALE (RURAL)TRANSGENDER (RURAL)SEX RATIO (RURAL)AVG HOUSEHOLD SIZE (RURAL)POPULATION 1998 (RURAL)ANNUAL GROWTH RATE (RURAL)ALL SEXES (URBAN)MALE (URBAN)FEMALE (URBAN)TRANSGENDER (URBAN)SEX RATIO (URBAN)AVG HOUSEHOLD SIZE (URBAN)POPULATION 1998 (URBAN)ANNUAL GROWTH RATE (URBAN)
518KPK/FATAKOHAT DIVISIONORAKZAI DISTRICTLOWER TEHSIL565.010732353767535506100.418.16654162.6300000.00.000.0
519KPK/FATAKOHAT DIVISIONORAKZAI DISTRICTUPPER TEHSIL299.06388532299315824102.277.04840601.4300000.00.000.0
520KPK/FATADERA ISMAIL KHAN DIVISIONSOUTH WAZIRISTAN DISTRICTBIRMIL TEHSIL923.0104282543234994811108.769.44585123.0800000.00.000.0
521KPK/FATADERA ISMAIL KHAN DIVISIONSOUTH WAZIRISTAN DISTRICTLADHA TEHSIL466.011084257755530807108.816.36722782.2700000.00.000.0
522KPK/FATADERA ISMAIL KHAN DIVISIONSOUTH WAZIRISTAN DISTRICTMAKIN TEHSIL404.05864630906277382111.426.35305283.4900000.00.000.0
523KPK/FATADERA ISMAIL KHAN DIVISIONSOUTH WAZIRISTAN DISTRICTSARAROGHA TEHSIL813.09818051479466947110.257.57720631.6400000.00.000.0
524KPK/FATADERA ISMAIL KHAN DIVISIONSOUTH WAZIRISTAN DISTRICTSERWEKAI TEHSIL398.05454028695258441111.037.20314722.9300000.00.000.0
525KPK/FATADERA ISMAIL KHAN DIVISIONSOUTH WAZIRISTAN DISTRICTTIARZA TEHSIL734.04515623649215070109.967.99377080.9500000.00.000.0
526KPK/FATADERA ISMAIL KHAN DIVISIONSOUTH WAZIRISTAN DISTRICTTOI KHULLA TEHSIL567.05041327462229474119.689.34365081.7100000.00.000.0
527KPK/FATADERA ISMAIL KHAN DIVISIONSOUTH WAZIRISTAN DISTRICTWANA TEHSIL2315.0153156813427179618113.3010.19907722.7900000.00.000.0